Triple
T26137962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruqaʿa |
E659432
|
entity |
| Predicate | letterConnection |
P150002
|
FINISHED |
| Object | most letters connected within words |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: most letters connected within words | Statement: [Ruqaʿa, letterConnection, most letters connected within words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: letterConnection Context triple: [Ruqaʿa, letterConnection, most letters connected within words]
-
A.
letterSequence
Indicates that one sequence of letters directly follows or is ordered in relation to another within a larger string or alphabetic arrangement.
-
B.
letterRepresents
Indicates that a particular letter or character stands for, symbolizes, or denotes a specific value, concept, or entity.
-
C.
letterGroups
Indicates that entities are organized or associated into specific groups based on letters or letter-based criteria.
-
D.
alphabet
Indicates that one entity is an alphabet or set of symbols used for representing elements (such as characters or tokens) in relation to another entity.
-
E.
hasLetterforms
chosen
Indicates a relationship where one entity possesses or includes specific letterforms as part of its written or typographic representation.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ee5bc3c20c8190bf2cf272f4170e95 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f60be1d2408190820365bf7d8436bd |
completed | May 2, 2026, 2:36 p.m. |
| PD | Predicate disambiguation | batch_69f5b0021da88190bdd4cf2698c23edf |
completed | May 2, 2026, 8:04 a.m. |
Created at: April 26, 2026, 8:18 p.m.